Dominance Norms and Data for Spoken Ambiguous Words in British English

New pre-print from @BeckyAGilbert and @JenniRodd:

We collated data from a number of published experiments and pre-tests to construct a dataset of 29,533 valid word association responses for 243 spoken ambiguous words from participants from the United Kingdom. We provide summary dominance data for the 182 ambiguous words that have a minimum of 100 responses, and a tool for automatically coding new word association responses based on responses in our coded set, which allows additional data to be more easily scored and added to this database. All files can be found at:

Learning new word meanings from story reading: the benefit of immediate testing

This work, led by Rachael Hulme and recently published in PeerJ explores vocabulary learning in adults from stories that were read in their native language.

A set of three experiments found that

  • New words learned incidentally through stories were less susceptible to forgetting over 24 hours compared with a more intentional vocab learning paradigm.
  • Vocab learning was strongly boosted when participants completed a brief test of the new vocab following story reading

See here for a twitter thread that summarises the key message.

Hulme RC, Rodd JM. 2021. Learning new word meanings from story reading: the benefit of immediate testing. PeerJ 9:e11693

University of Oxford: Applied Linguistics Lunchtime Seminar Series

Prof Jenni Rodd is giving a talk at the Department of Education, University of Oxford

24th May 2021 12-1pm.

‘Settling Into Semantic Space: An Ambiguity-Focused Account of Word-Meaning

Most words are ambiguous: individual wordforms (e.g., “run”) can map onto multiple different interpretations depending on their sentence context (e.g., “the athlete/politician/river runs”). Models of word-meaning access must therefore explain how listeners and readers are able to rapidly settle on a single, contextually appropriate meaning for each word that they encounter. I will present a new account of word-meaning access that places semantic disambiguation at its core.

The model has three key characteristics. (i) Lexical-semantic knowledge is viewed as a high-dimensional space; familiar word meanings correspond to stable states within this lexical-semantic space. (ii) Multiple linguistic and paralinguistic cues can influence the settling process by which the system resolves on one of these familiar meanings. (iii) Learning mechanisms play a vital role in facilitating rapid word-meaning access by shaping and maintaining high quality lexical-semantic knowledge.

More information here